What is Amnesia?

Amnesia operates entirely on-premise, without transmitting any data over the internet. This ensures maximum data security and eliminates administrative hurdles associated with sharing personal or sensitive information.

Masking in data anonymization is a technique used to hide or obscure sensitive information within a dataset so that the original data cannot be easily retrieved or identified. It's one of the many methods used to protect personally identifiable information (PII) while still allowing data to be used for certain purposes like testing, analytics, or training. Amnesia offers customizable masking templates that can provide solutions easily tailored to the needs of the user and the intricacies of the data.

Masking in data anonymization is a technique used to hide or obscure sensitive information within a dataset so that the original data cannot be easily retrieved or identified. It's one of the many methods used to protect personally identifiable information (PII) while still allowing data to be used for certain purposes like testing, analytics, or training. Amnesia offers customizable masking templates that can provide solutions easily tailored to the needs of the user and the intricacies of the data. K - anonymity is a real privacy guarantee, proved and established mathematically. It guarantees that every combination of values of quasi - identifiers can be indistinctly matched to at least k persons. In order to achieve that, quasi - identifiers are pooled in a larger group that contains information corresponding to any single person. K refers to the occurrences of each combination of quasi - identifiers in the dataset. If k=3, the dataset is 3 - anonymous, meaning at least three records (persons) share the same quasi - identifier values. Pooling the quasi - identifiers in larger groups is achieved through generalization, hence reducing an attribute's specificity by subs tituting a specific value for a more general one. For example, specific age values can be generalized into age groups (e.g., the group 30 - 35 includes the age values of 30, 31, 32, 33, 34, 35). In the case of data values that are irrelevant to the purpose of the data collection, k - anonymity can be achieved by using suppression complementary to generalization. Suppression refers to removing an attribute's value entirely from the dataset. Amnesia offers powerful and flexible algorithms for k - anonymization.

Km - anonymity is a relaxed variant of k - anonymity, specifically designed to perform better in high - dimensional datasets. Like k - anonymity, it operates on a set of n quasi - identifiers. However, it limits the privacy guarant ee to adversaries who have knowledge of only m quasi - identifiers, where m is significantly smaller than n ( m ≪ n ). In essence, the algorithm ensures that for any combination of m quasi - identifiers, there are at least k records in the dataset sharing that c ombination. This guarantee holds independently of the total number of quasi - identifiers, enabling more scalable anonymization while still providing meaningful privacy protection.